Associations Between Mobility Indices and the COVID-19 Pandemic in Thailand
Article Sidebar
Main Article Content
Abstract
This study aims to examine the associations between the Coronavirus disease 2019 (COVID-19) pandemic and alternative indicators. Specifically, Apple mobility index, Google community mobility index, and Nighttime-light (NTL) data are used for empirical analyses using ordinary least squares (OLS) and panel regressions as research methods. Results produced by OLS models show that Apple’s subcategory of driving activity and Google’s subcategory of visiting transit places are negatively associated with the number of COVID-19 cases. To extend the spatiotemporal details of this analysis, we formulate the panel data by integrating the monthly provincial indicators of Apple mobility index, NTL index, and the COVID-19 infected cases. Both fixed- and random-effects panel regression models indicate that Apple’s driving and walking mobility subcategories are negatively associated with the COVID-19 infected cases. By contrast, the relationship between the NTL index and the intensity of the COVID-19 outbreak is inconclusive. These findings suggest that Apple's mobility index can be applied as an alternative and timely indicator of economic activity, particularly for observing the near real-time intensity of mobility and transportation volume. In addition, these findings can serve as a resource for developing spatial models for urban planning and geographical impacts.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
References
Almagro, M., & Orane-Hutchinson, A. (2022). JUE Insight: The determinants of the differential exposure to COVID-19 in New York city and their evolution over time. Journal of Urban Economics, 127, 103293. https://doi.org/https://doi.org/10.1016/j.jue.2020.103293
Badr, H. S., Du, H., Marshall, M., Dong, E., Squire, M. M., & Gardner, L. M. (2020). Association between mobility patterns and COVID-19 transmission in the USA: A mathematical modelling study. The Lancet Infectious Diseases, 20(11), 1247-1254. https://doi.org/10.1016/S1473-3099(20)30553-3
Camba, A. C. J., & Camba, A. L. (2020). The effects of restrictions in economic activity on the spread of COVID-19 in the Philippines: Insights from Apple and Google mobility indicators. The Journal of Asian Finance, Economics and Business, 7(12), 115-121. https://doi.org/10.13106/JAFEB.2020.VOL7.NO12.115
Chan, J. Y., Leow, S. M., Bea, K. T., Cheng, W. K., Phoong, S. W., Hong, Z.W., & Chen, Y.L. (2022). Mitigating the multicollinearity problem and its machine learning approach: A review. Mathematics, 10(8). https://doi.org/10.3390/math10081283
Chen, G. J. (2012). A simple way to deal with multicollinearity. Journal of Applied Statistics, 39(9), 1893-1909. https://doi.org/10.1080/02664763.2012.690857
Chiou, L., & Tucker, C. (2020). Social distancing, internet access and inequality. National Bureau of Economic Research Working Paper Series, No. 26982. https://doi.org/10.3386/w26982
Fang, H., Wang, L., & Yang, Y. (2020). Human mobility restrictions and the spread of the Novel Coronavirus (2019-nCoV) in China. Journal of Public Economics, 191, 104272. https://doi.org/https://doi.org/10.1016/j.jpubeco.2020.104272
Fauver, J. R., Petrone, M. E., Hodcroft, E. B., Shioda, K., Ehrlich, H. Y., Watts, A. G., Vogels, C. B. F., Brito, A. F., Alpert, T., Muyombwe, A., Razeq, J., Downing, R., Cheemarla, N. R., Wyllie, A. L., Kalinich, C. C., Ott, I. M., Quick, J., Loman, N. J., Neugebauer, K. M., . . . Grubaugh, N. D. (2020). Coast-to-coast spread of SARS-CoV-2 during the early epidemic in the United States. Cell, 181(5), 990-996.e995. https://doi.org/https://doi.org/10.1016/j.cell.2020.04.021
Haase, D., Lautenbach, S., & Seppelt, R. (2010). Modeling and simulating residential mobility in a shrinking city using an agent-based approach. Environmental Modelling & Software, 25(10), 1225-1240. https://doi.org/https://doi.org/10.1016/j.envsoft.2010.04.009
Hasan, S., & Ukkusuri, S. V. (2014). Urban activity pattern classification using topic models from online geo-location data. Transportation Research Part C: Emerging Technologies, 44, 363-381. https://doi.org/https://doi.org/10.1016/j.trc.2014.04.003
Hermes, K., & Poulsen, M. (2012). A review of current methods to generate synthetic spatial microdata using reweighting and future directions. Computers, Environment and Urban Systems, 36(4), 281-290. https://doi.org/https://doi.org/10.1016/j.compenvurbsys.2012.03.005
Holmdahl, I., & Buckee, C. (2020). Wrong but useful — What Covid-19 epidemiologic models can and cannot tell us. New England Journal of Medicine, 383(4), 303-305. https://doi.org/10.1056/NEJMp2016822
Huang, X., Li, Z., Jiang, Y., Li, X., & Porter, D. (2020). Twitter reveals human mobility dynamics during the COVID-19 pandemic. PLoS One, 15(11), e0241957. https://doi.org/10.1371/journal.pone.0241957
Huang, X., Wang, C., & Li, Z. (2018). Reconstructing flood inundation probability by enhancing near real-time imagery with real-time gauges and tweets. IEEE Transactions on Geoscience and Remote Sensing, 56, 4691-4701. https://doi.org/10.1109/TGRS.2018.2835306
Isaacman, S., Becker, R., Cáceres, R., Kobourov, S., Martonosi, M., Rowland, J., & Varshavsky, A. (2011). Identifying important places in people’s lives from cellular network data. In K. Lyons, J. Hightower, & E.M. Huang (Eds.), Pervasive 2011: Pervasive Computing. Springer. https://doi.org/10.1007/978-3-642-21726-5_9
Jiang, Y., Li, Z., & Ye, X. (2019). Understanding demographic and socioeconomic biases of geotagged Twitter users at the county level. Cartography and Geographic Information Science, 46(3), 228-242. https://doi.org/10.1080/15230406.2018.1434834
Kartal, M. T. (2021). The effect of COVID-19 pandemic on oil prices: Daily evidence from Turkey. Energy Research Letters,1(4), 1-4. https://doi.org/10.46557/001c.18723
Kraemer, M. U. G., Yang, C.-H., Gutierrez, B., Wu, C.-H., Klein, B., Pigott, D. M., null, n., du Plessis, L., Faria, N. R., Li, R., Hanage, W. P., Brownstein, J. S., Layan, M., Vespignani, A., Tian, H., Dye, C., Pybus, O. G., & Scarpino, S. V. (2020). The effect of human mobility and control measures on the COVID-19 epidemic in China. Science, 368(6490), 493-497. https://doi.org/10.1126/science.abb4218
Li, Z., Huang, X., ye, X., & Li, X. (2020). ODT flow explorer: Extract, query, and visualize human mobility. ArXiv:2011.12958. ARXVI. https://arxiv.org/abs/2011.12958
Lou, J., Shen, X., & Niemeier, D. (2020). Are stay-at-home orders more difficult to follow for low-income groups? Journal of Transport Geography, 89, 102894. https://doi.org/https://doi.org/10.1016/j.jtrangeo.2020.102894
Luenam, A., & Puttanapong, N. (2020). Modelling and analyzing spatial clusters of leptospirosis based on satellite-generated measurements of environmental factors in Thailand during 2013-2015. Geospat Health, 15(2), 217-224. https://doi.org/10.4081/gh.2020.856
Ma, J., Heppenstall, A., Harland, K., & Mitchell, G. (2014). Synthesising carbon emission for mega-cities: A static spatial microsimulation of transport CO2 from urban travel in Beijing. Computers, Environment and Urban Systems, 45, 78-88. https://doi.org/https://doi.org/10.1016/j.compenvurbsys.2014.02.006
Martín, Y., Cutter, S., & Li, Z. (2019). Bridging Twitter and survey data for the evacuation assessment of Hurricane Matthew and Hurricane Irma. Natural Hazards Review. https://doi.org/10.1061/(ASCE)NH.1527-6996.000035
Oh, J., Lee, H.-Y., Khuong, Q. L., Markuns, J. F., Bullen, C., Barrios, O. E. A., Hwang, S.-s., Suh, Y. S., McCool, J., Kachur, S. P., Chan, C.-C., Kwon, S., Kondo, N., Hoang, V. M., Moon, J. R., Rostila, M., Norheim, O. F., You, M., Withers, M., . . . Gostin, L. O. (2021). Mobility restrictions were associated with reductions in COVID-19 incidence early in the pandemic: Evidence from a real-time evaluation in 34 countries. Scientific Reports, 11(1), 13717. https://doi.org/10.1038/s41598-021-92766-z
Oliver, N., Letouzé, E., Sterly, H., Delataille, S., De Nadai, M., Lepri, B., Lambiotte, R., Benjamins, R., Cattuto, C., Colizza, V., Cordes, N. d., Fraiberger, S. P., Koebe, T., Lehmann, S., Murillo, J., Pentland, A. S., Pham, P. N., Pivetta, F., Salah, A. A., . . . Vinck, P. (2020). Mobile phone data and COVID-19: Missing an opportunity? ArXiv: 2003.12347. ARXIV. https://arxiv.org/abs/2003.12347
Osewe, P. (2021). Pandemic preparedness and response strategies: COVID-19 lessons from the Republic of Korea, Thailand, and Viet Nam. Asian Development Bank. https://www.adb.org/sites/default/files/publication/743746/pandemic-preparedness-covid-19-lessons.pdf
Pei, S., Kandula, S., & Shaman, J. (2020). Differential effects of intervention timing on COVID-19 spread in the United States. MedRxiv. https://doi.org/10.1101/2020.05.15.20103655
Puttanapong, N., Martinez, A., Bulan, J. A., Addawe, M., Durante, R. L., & Martillan, M. (2022). Predicting poverty using geospatial data in Thailand. ISPRS International Journal of Geo-Information, 11(5). https://doi.org/10.3390/ijgi11050293
Sangkasem, K., & Puttanapong, N. (2022). Analysis of spatial inequality using DMSP-OLS nighttime-light satellite imageries: A case study of Thailand. Regional Science Policy & Practice, 14(4), 828-849. https://doi.org/https://doi.org/10.1111/rsp3.12386
Sen, S., Karaca-Mandic, P., & Georgiou, A. (2020). Association of stay-at-home orders with COVID-19 hospitalizations in 4 states. JAMA, 323(24), 2522-2524. https://doi.org/10.1001/jama.2020.9176
Sills, J., Buckee, C. O., Balsari, S., Chan, J., Crosas, M., Dominici, F., Gasser, U., Grad, Y. H., Grenfell, B., Halloran, M. E., Kraemer, M. U. G., Lipsitch, M., Metcalf, C. J. E., Meyers, L. A., Perkins, T. A., Santillana, M., Scarpino, S. V., Viboud, C., Wesolowski, A., & Schroeder, A. (2020). Aggregated mobility data could help fight COVID-19. Science, 368(6487), 145-146. https://doi.org/10.1126/science.abb8021
Sirkeci, I., & Yucesahin, M. M. (2020). Coronavirus and migration: Analysis of human mobility and the spread of Covid-19. Migration Letters, 17(2), 379-398. https://doi.org/10.33182/ml.v17i2.935
Subbiah, R., Lum, K., Marathe, A., & Marathe, M. (2013). Activity based energy demand modeling for residential buildings. IEEE PES Innovative Smart Grid Technologies Conference (ISGT), (pp. 1-6). http://dx.doi.org/10.1109/ISGT.2013.6497822
Sulyok, M., & Walker, M. D. (2021). Mobility and COVID-19 mortality across Scandinavia: A modeling study. Travel Medicineand Infectious Disease, 41, 102039. https://doi.org/10.1016/j.tmaid.2021.102039
Tian, H., Liu, Y., Li, Y., Wu, C.-H., Chen, B., Kraemer, M. U. G., Li, B., Cai, J., Xu, B., Yang, Q., Wang, B., Yang, P., Cui, Y., Song, Y., Zheng, P., Wang, Q., Bjornstad, O. N., Yang, R., Grenfell, B. T., . . . Dye, C. (2020). An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China. Science, 368(6491), 638-642. https://doi.org/10.1126/science.abb6105
Wang, Y., Yuan, N., Lian, D., Xu, L., Xie, X., Chen, E., & Rui, Y. (2015). Regularity and conformity. Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Sydney, NSW, Australia. Association for Computing Machinery (pp. 1275-1284). https://doi.org/10.1145/2783258.2783350
World Health Organization. (2020). WHO Thailand situation report - 1. WHO. https://cdn.who.int/media/docs/default-source/searo/thailand/20200206-tha-sitrep-01-ncov-final.pdf?sfvrsn=7c8cb671_0
World Health Organization. (2021). WHO Thailand situation report - 201.WHO. https://cdn.who.int/media/docs/default-source/searo/thailand/2021_09_16_eng-sitrep-201-covid19.pdf?sfvrsn=3a249ade_5
World Health Organization. (2022). Coronavirus disease (COVID-19) weekly epidemiological update and weekly operational update. WHO. https://www.who.int/publications/m/item/weekly-epidemiological-update-on-covid-19---15-june-2022
Wu, L., Zhi, Y., Sui, Z., & Liu, Y. (2014). Intra-urban human mobility and activity transition: Evidence from social media check-in data. PLoS One, 9(5), e97010. https://doi.org/10.1371/journal.pone.0097010
Xu, S., & Li, Y. (2020). Beware of the second wave of COVID-19. The Lancet, 395(10233), 1321-1322. https://doi.org/10.1016/S0140-6736(20)30845-X
Yang, C., Sha, D., Liu, Q., Li, Y., Lan, H., Guan, W. W., Hu, T., Li, Z., Zhang, Z., Thompson, J. H., Wang, Z., Wong, D., Ruan, S., Yu, M., Richardson, D., Zhang, L., Hou, R., Zhou, Y., Zhong, C., . . . Ding, A. (2020). Taking the pulse of COVID-19: A spatiotemporal perspective. International Journal of Digital Earth, 13(10), 1186-1211. https://doi.org/10.1080/17538947.2020.1809723
Zhang, D., Huang, J., Li, Y., Zhang, F., Xu, C.-Z., & He, T. (2014). Exploring human mobility with multi-source data at extremely large metropolitan scales. MobiCom'14: The 20th Annual International Conference on Mobile Computing and Networking (pp. 201-212). Association for Computing Machinery. https://doi.org/10.1145/2639108.2639116